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Loss of synchrony in an inhibitory network of type-I oscillators.

机译:I型振荡器抑制网络中的同步丢失。

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摘要

Synchronization of excitable cells coupled by reciprocal inhibition is a topic of significant interest due to the important role that inhibitory synaptic interaction plays in the generation and regulation of coherent rhythmic activity in a variety of neural systems. While recent work revealed the synchronizing influence of inhibitory coupling on the dynamics of many networks, it is known that strong coupling can destabilize phase-locked firing. Here we examine the loss of synchrony caused by an increase in inhibitory coupling in networks of type-I Morris-Lecar model oscillators, which is characterized by a period-doubling cascade and leads to mode-locked states with alternation in the firing order of the two cells, as reported recently by Maran and Canavier (2007) for a network of Wane Buzsaki model neurons. Although alternating-order firing has been previously reported as a near-synchronous state, we show that the stable phase difference between the spikes of the two Morris-Lecar cells can constitute as much as 70% of the unperturbed oscillation period. Further, we examine the generality of this phenomenon for a class of type-I oscillators that are close to their excitation thresholds, and provide an intuitive geometric description of such "leap-frog" dynamics. In the Morris-Lecar model network, the alternation in the firing order arises under the condition of fast closing of K+ channels at hyperpolarized potentials, which leads to slow dynamics of membrane potential upon synaptic inhibition, allowing the presynaptic cell to advance past the postsynaptic cell in each cycle of the oscillation. Further, we show that non-zero synaptic decay time is crucial for the existence of leapfrog firing in networks of phase oscillators. However, we demonstrate that leap-frog spiking can also be obtained in pulse-coupled inhibitory networks of one-dimensional dimensional oscillators with a multi-branched phase domain, for instance in a network of quadratic integrate-and-fire model cells. Also, we show that the entire bifurcation structure of the network can be explained by a simple scaling of the STRC (spike-time response curve) amplitude, using a simplified quadratic STRC as an example, and derive the general conditions on the shape of the STRC function that leads to leap-frog firing. Further, for the case of a homogeneous network, we establish quantitative conditions on the phase resetting properties of each cell necessary for stable alternating-order spiking, complementing the analysis of Goel and Ermentrout (2002) of the order-preserving phase transition map. We show that the extension of STRC to negative values of phase is necessary to predict the response of a model cell to several close non-weak perturbations. This allows us for instance to accurately describe the dynamics of non-weakly coupled network of three model cells. Finally, the phase return map is also extended to the heterogenous network, and is used to analyze both the order-alternating firing and the order-preserving non-zero phase locked state in this case.
机译:由于相互抑制,可兴奋细胞的同步是一个重要的话题,因为抑制突触相互作用在各种神经系统中,在连贯的节律活动的产生和调节中起着重要的作用。尽管最近的工作揭示了抑制耦合对许多网络动力学的同步影响,但众所周知,强耦合会破坏锁相发射的稳定性。在这里,我们研究了由I型Morris-Lecar模型振荡器网络中抑制耦合的增加引起的同步损失,其特征是周期倍增级联并导致锁模态的激发顺序发生交替。正如Maran和Canavier(2007)最近为Wane Buzsaki模型神经元网络所报道的那样,有两个细胞。尽管以前已经报道过交替点火是一种接近同步状态,但我们表明,两个莫里斯-莱卡尔电池的尖峰之间的稳定相位差可构成无扰动振荡周期的70%。此外,我们针对接近其激励阈值的一类I型振荡器检查了这种现象的普遍性,并提供了这种“跳跃式”动力学的直观几何描述。在Morris-Lecar模型网络中,在超极化电势下K +通道快速关闭的情况下,触发顺序发生了交替变化,这导致突触抑制后膜电势的缓慢动态变化,从而使突触前细胞前进超过突触后细胞。在每个振荡周期中。此外,我们表明,非零突触衰减时间对于相位振荡器网络中跳越触发的存在至关重要。但是,我们证明,在具有多分支相域的一维维振荡器的脉冲耦合抑制网络中,例如在二次积分和发射模型单元网络中,也可以获得跳跃式尖峰信号。同样,我们表明,网络的整个分叉结构可以通过简单地缩放STRC(尖峰时间响应曲线)幅度来解释,以简化的二次STRC为例,并得出关于网络形状的一般条件。 STRC功能可导致跳越射击。此外,对于同构网络,我们在稳定交替交替阶峰值所需的每个单元的相位重置特性上建立定量条件,补充了Goel和Ermentrout(2002)对阶数保持相变图的分析。我们表明,将STRC扩展到相位的负值对于预测模型单元对几个紧密的非弱摄动的响应是必要的。例如,这使我们能够准确地描述三个模型单元的非弱耦合网络的动力学。最后,相位返回图也被扩展到异构网络,并且在这种情况下被用于分析顺序交替触发和顺序保持非零锁相状态。

著录项

  • 作者

    Oh, Myongkeun.;

  • 作者单位

    New Jersey Institute of Technology.;

  • 授予单位 New Jersey Institute of Technology.;
  • 学科 Applied Mathematics.;Biology Neuroscience.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 101 p.
  • 总页数 101
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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